Longitudinal changes in health-related quality of life during concussion recovery among youth athletes
Bibliographic record
Abstract
Objective Determine longitudinal changes in health-related quality of life (HRQoL) in youth during recovery from a sport-related concussion. Design Prospective case-series. Participants 68 youth (13–18 years; 62% male) with an acute sport-related concussion were enrolled with 5 lost to follow-up. Intervention (or assessment of risk factors) Subsequently developing Post-Concussion Syndrome (PCS: symptomatic after 30 days), initial symptom severity (Post Concussion Symptom Scale), concussion history, sex, age, and academic accommodations during recovery. Outcome measures PedsQL Cognitive Functioning Scale and PedsQL 4.0 measured physical, social, and emotional domains of HRQoL. They were administered before each appointment until medical clearance. Multivariable mixed modelling accounted for repeated measures within an athlete and expressed as point increase per week. Main results Initial mean HRQoL was highest for social (non-PCS: 92.7 out of 100; PCS: 86.3), followed by emotional (non-PCS: 86.45; PCS: 69.7), physical (non-PCS: 80.95; PCS: 49.2), and cognitive domains (non-PCS: 69.1; PCS: 50.3). There was effect modification by developing PCS. Changes in HRQoL subscores were: cognitive functioning non-PCS (+9.8 points [95% CI: 6.9, 12.7]) and PCS (+4.2 points [95% CI: 2.8, 5.6]); social HRQoL non-PCS (+2.3 points [1.0, 4.0]) and PCS (+0.5 points [0.1, 1.0); emotional HRQoL non-PCS (+5.0 points [95% CI: 2.9, 7.0]) and PCS points (+2.2 [1.6, 2.9]); and physical non-PCS (+6.3 points [95% CI: [3.5, 9.2]) and PCS (+3.3 points (95% CI: 2.5, 4.2). Conclusions HRQoL significantly improved longitudinally with greater improvement among non-PCS athletes. HRQoL impairment was most apparent in the cognitive and physical domains. Competing interests None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".